01_Deep_Learning_mnist_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "from keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels),(test_images, test_labels) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "too many indices for array",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-36-46edfbb8c668>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_images\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbinary\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: too many indices for array"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(train_images[1,:,:], cmap = plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_images[1,:,:], cmap = plt.cm.binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images.reshape((60000,28*28))\n",
    "train_images = train_images.astype(\"float32\")/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_images = test_images.reshape((10000,28*28))\n",
    "test_images = test_images.astype(\"float32\")/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 784)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import to_categorical\n",
    "train_labels = to_categorical(train_labels)\n",
    "test_labels = to_categorical(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import models\n",
    "from keras import layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = models.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add(layers.Dense(512, activation = \"relu\", input_shape = (28*28, )))\n",
    "model.add(layers.Dense(10, activation = \"softmax\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 407,050\n",
      "Trainable params: 407,050\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer= \"rmsprop\",\n",
    "             loss = \"categorical_crossentropy\",\n",
    "             metrics = [\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 5s 87us/step - loss: 0.2560 - accuracy: 0.9260\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 4s 73us/step - loss: 0.1034 - accuracy: 0.9692\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 4s 72us/step - loss: 0.0679 - accuracy: 0.9799\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 4s 72us/step - loss: 0.0494 - accuracy: 0.9849\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 4s 73us/step - loss: 0.0373 - accuracy: 0.9888\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x23ce54932b0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images,\n",
    "         train_labels,\n",
    "         epochs = 5,\n",
    "         batch_size = 128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 51us/step\n",
      "test_loss :  0.06573833876058925 \n",
      "test_acc :  0.980400025844574\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "print(\"test_loss : \", test_loss,\"\\ntest_acc : \", test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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